Overview

Dataset statistics

Number of variables10
Number of observations214
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory16.8 KiB
Average record size in memory80.6 B

Variable types

NUM10

Warnings

Dataset has 1 (0.5%) duplicate rows Duplicates
Mg has 42 (19.6%) zeros Zeros
K has 30 (14.0%) zeros Zeros
Ba has 176 (82.2%) zeros Zeros
Fe has 144 (67.3%) zeros Zeros

Reproduction

Analysis started2021-11-15 15:19:31.641191
Analysis finished2021-11-15 15:19:50.965063
Duration19.32 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

RI
Real number (ℝ≥0)

Distinct178
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.518365421
Minimum1.51115
Maximum1.53393
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-11-15T16:19:51.070746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.51115
5-th percentile1.515401
Q11.5165225
median1.51768
Q31.5191575
95-th percentile1.523664
Maximum1.53393
Range0.02278
Interquartile range (IQR)0.002635

Descriptive statistics

Standard deviation0.003036863739
Coefficient of variation (CV)0.002000087527
Kurtosis4.931737386
Mean1.518365421
Median Absolute Deviation (MAD)0.001265
Skewness1.625430506
Sum324.9302
Variance9.222541372e-06
MonotocityNot monotonic
2021-11-15T16:19:51.263264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.5215231.4%
 
1.5164531.4%
 
1.515931.4%
 
1.5221320.9%
 
1.5176320.9%
 
1.5177920.9%
 
1.5176920.9%
 
1.5179320.9%
 
1.5161320.9%
 
1.5161820.9%
 
Other values (168)19189.3%
 
ValueCountFrequency (%) 
1.5111510.5%
 
1.5113110.5%
 
1.5121510.5%
 
1.5129910.5%
 
1.5131610.5%
 
ValueCountFrequency (%) 
1.5339310.5%
 
1.5312510.5%
 
1.5277710.5%
 
1.5273910.5%
 
1.5272510.5%
 

Na
Real number (ℝ≥0)

Distinct142
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.40785047
Minimum10.73
Maximum17.38
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-11-15T16:19:51.419839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10.73
5-th percentile12.415
Q112.9075
median13.3
Q313.825
95-th percentile14.8535
Maximum17.38
Range6.65
Interquartile range (IQR)0.9175

Descriptive statistics

Standard deviation0.8166035557
Coefficient of variation (CV)0.06090488238
Kurtosis3.052232409
Mean13.40785047
Median Absolute Deviation (MAD)0.435
Skewness0.4541814537
Sum2869.28
Variance0.6668413672
MonotocityNot monotonic
2021-11-15T16:19:51.570410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1352.3%
 
13.0252.3%
 
13.2152.3%
 
12.8541.9%
 
13.6441.9%
 
13.2441.9%
 
12.8641.9%
 
13.3341.9%
 
12.9331.4%
 
13.231.4%
 
Other values (132)17380.8%
 
ValueCountFrequency (%) 
10.7310.5%
 
11.0210.5%
 
11.0310.5%
 
11.2310.5%
 
11.4510.5%
 
ValueCountFrequency (%) 
17.3810.5%
 
15.7910.5%
 
15.1510.5%
 
15.0110.5%
 
14.9910.5%
 

Mg
Real number (ℝ≥0)

ZEROS

Distinct94
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.68453271
Minimum0
Maximum4.49
Zeros42
Zeros (%)19.6%
Memory size1.7 KiB
2021-11-15T16:19:51.778887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.115
median3.48
Q33.6
95-th percentile3.85
Maximum4.49
Range4.49
Interquartile range (IQR)1.485

Descriptive statistics

Standard deviation1.442407845
Coefficient of variation (CV)0.5373031364
Kurtosis-0.4103189629
Mean2.68453271
Median Absolute Deviation (MAD)0.205
Skewness-1.152559318
Sum574.49
Variance2.080540391
MonotocityNot monotonic
2021-11-15T16:19:51.926484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04219.6%
 
3.5483.7%
 
3.4883.7%
 
3.5883.7%
 
3.5273.3%
 
3.6252.3%
 
3.541.9%
 
3.6641.9%
 
3.6141.9%
 
3.5641.9%
 
Other values (84)12056.1%
 
ValueCountFrequency (%) 
04219.6%
 
0.3310.5%
 
0.7810.5%
 
1.0110.5%
 
1.3510.5%
 
ValueCountFrequency (%) 
4.4910.5%
 
3.9810.5%
 
3.9710.5%
 
3.9310.5%
 
3.931.4%
 

Al
Real number (ℝ≥0)

Distinct118
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.444906542
Minimum0.29
Maximum3.5
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-11-15T16:19:52.096039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.696
Q11.19
median1.36
Q31.63
95-th percentile2.394
Maximum3.5
Range3.21
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.4992696456
Coefficient of variation (CV)0.3455376739
Kurtosis2.060568969
Mean1.444906542
Median Absolute Deviation (MAD)0.21
Skewness0.907289809
Sum309.21
Variance0.249270179
MonotocityNot monotonic
2021-11-15T16:19:52.254613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.5483.7%
 
1.1962.8%
 
1.2952.3%
 
1.4352.3%
 
1.2352.3%
 
1.5652.3%
 
1.3641.9%
 
1.3541.9%
 
1.2841.9%
 
1.6231.4%
 
Other values (108)16577.1%
 
ValueCountFrequency (%) 
0.2910.5%
 
0.3410.5%
 
0.4720.9%
 
0.5110.5%
 
0.5620.9%
 
ValueCountFrequency (%) 
3.510.5%
 
3.0410.5%
 
3.0210.5%
 
2.8810.5%
 
2.7910.5%
 

Si
Real number (ℝ≥0)

Distinct133
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.65093458
Minimum69.81
Maximum75.41
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-11-15T16:19:52.411189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum69.81
5-th percentile71.315
Q172.28
median72.79
Q373.0875
95-th percentile73.5175
Maximum75.41
Range5.6
Interquartile range (IQR)0.8075

Descriptive statistics

Standard deviation0.7745457948
Coefficient of variation (CV)0.0106611952
Kurtosis2.967902956
Mean72.65093458
Median Absolute Deviation (MAD)0.385
Skewness-0.7304472251
Sum15547.3
Variance0.5999211882
MonotocityNot monotonic
2021-11-15T16:19:52.549824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
72.8641.9%
 
73.2841.9%
 
73.141.9%
 
72.9941.9%
 
73.1141.9%
 
72.9731.4%
 
72.9531.4%
 
73.0131.4%
 
72.6431.4%
 
72.9631.4%
 
Other values (123)17983.6%
 
ValueCountFrequency (%) 
69.8110.5%
 
69.8910.5%
 
70.1610.5%
 
70.2610.5%
 
70.4310.5%
 
ValueCountFrequency (%) 
75.4110.5%
 
75.1810.5%
 
74.5510.5%
 
74.4510.5%
 
73.8810.5%
 

K
Real number (ℝ≥0)

ZEROS

Distinct65
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4970560748
Minimum0
Maximum6.21
Zeros30
Zeros (%)14.0%
Memory size1.7 KiB
2021-11-15T16:19:52.702409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1225
median0.555
Q30.61
95-th percentile0.76
Maximum6.21
Range6.21
Interquartile range (IQR)0.4875

Descriptive statistics

Standard deviation0.6521918456
Coefficient of variation (CV)1.312109194
Kurtosis54.68969853
Mean0.4970560748
Median Absolute Deviation (MAD)0.115
Skewness6.55164831
Sum106.37
Variance0.4253542034
MonotocityNot monotonic
2021-11-15T16:19:53.005606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03014.0%
 
0.57125.6%
 
0.6115.1%
 
0.56115.1%
 
0.58104.7%
 
0.6483.7%
 
0.6183.7%
 
0.5973.3%
 
0.5462.8%
 
0.6262.8%
 
Other values (55)10549.1%
 
ValueCountFrequency (%) 
03014.0%
 
0.0210.5%
 
0.0310.5%
 
0.0420.9%
 
0.0510.5%
 
ValueCountFrequency (%) 
6.2120.9%
 
2.710.5%
 
1.7610.5%
 
1.6810.5%
 
1.4610.5%
 

Ca
Real number (ℝ≥0)

Distinct143
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.956962617
Minimum5.43
Maximum16.19
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-11-15T16:19:53.171131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.43
5-th percentile7.8125
Q18.24
median8.6
Q39.1725
95-th percentile11.5615
Maximum16.19
Range10.76
Interquartile range (IQR)0.9325

Descriptive statistics

Standard deviation1.423153487
Coefficient of variation (CV)0.1588879566
Kurtosis6.681977951
Mean8.956962617
Median Absolute Deviation (MAD)0.445
Skewness2.047053913
Sum1916.79
Variance2.025365848
MonotocityNot monotonic
2021-11-15T16:19:53.325743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.0352.3%
 
8.4352.3%
 
9.5741.9%
 
8.4441.9%
 
8.7941.9%
 
8.3831.4%
 
8.8331.4%
 
8.6731.4%
 
8.3931.4%
 
8.5331.4%
 
Other values (133)17782.7%
 
ValueCountFrequency (%) 
5.4310.5%
 
5.7910.5%
 
5.8710.5%
 
6.4710.5%
 
6.6510.5%
 
ValueCountFrequency (%) 
16.1910.5%
 
14.9610.5%
 
14.6810.5%
 
14.410.5%
 
13.4410.5%
 

Ba
Real number (ℝ≥0)

ZEROS

Distinct34
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.175046729
Minimum0
Maximum3.15
Zeros176
Zeros (%)82.2%
Memory size1.7 KiB
2021-11-15T16:19:53.477338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.57
Maximum3.15
Range3.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4972192606
Coefficient of variation (CV)2.840494441
Kurtosis12.54108358
Mean0.175046729
Median Absolute Deviation (MAD)0
Skewness3.416424569
Sum37.46
Variance0.2472269931
MonotocityNot monotonic
2021-11-15T16:19:53.608985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
017682.2%
 
1.5720.9%
 
0.6420.9%
 
0.0920.9%
 
1.5920.9%
 
0.1120.9%
 
0.1510.5%
 
1.5510.5%
 
0.6110.5%
 
0.6310.5%
 
Other values (24)2411.2%
 
ValueCountFrequency (%) 
017682.2%
 
0.0610.5%
 
0.0920.9%
 
0.1120.9%
 
0.1410.5%
 
ValueCountFrequency (%) 
3.1510.5%
 
2.8810.5%
 
2.210.5%
 
1.7110.5%
 
1.6810.5%
 

Fe
Real number (ℝ≥0)

ZEROS

Distinct32
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05700934579
Minimum0
Maximum0.51
Zeros144
Zeros (%)67.3%
Memory size1.7 KiB
2021-11-15T16:19:53.751604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.267
Maximum0.51
Range0.51
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.09743870064
Coefficient of variation (CV)1.709170651
Kurtosis2.662015617
Mean0.05700934579
Median Absolute Deviation (MAD)0
Skewness1.75432747
Sum12.2
Variance0.009494300382
MonotocityNot monotonic
2021-11-15T16:19:53.892228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
014467.3%
 
0.1773.3%
 
0.2473.3%
 
0.0962.8%
 
0.152.3%
 
0.1141.9%
 
0.0731.4%
 
0.1431.4%
 
0.2831.4%
 
0.1631.4%
 
Other values (22)2913.6%
 
ValueCountFrequency (%) 
014467.3%
 
0.0110.5%
 
0.0310.5%
 
0.0510.5%
 
0.0610.5%
 
ValueCountFrequency (%) 
0.5110.5%
 
0.3710.5%
 
0.3510.5%
 
0.3410.5%
 
0.3210.5%
 

Type
Real number (ℝ≥0)

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.780373832
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-11-15T16:19:54.013903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.103738646
Coefficient of variation (CV)0.7566387736
Kurtosis-0.2795182977
Mean2.780373832
Median Absolute Deviation (MAD)1
Skewness1.114915201
Sum595
Variance4.425716292
MonotocityIncreasing
2021-11-15T16:19:54.113642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
27635.5%
 
17032.7%
 
72913.6%
 
3177.9%
 
5136.1%
 
694.2%
 
ValueCountFrequency (%) 
17032.7%
 
27635.5%
 
3177.9%
 
5136.1%
 
694.2%
 
ValueCountFrequency (%) 
72913.6%
 
694.2%
 
5136.1%
 
3177.9%
 
27635.5%
 

Interactions

2021-11-15T16:19:37.106392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.255718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.378391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.492087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.626727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.748429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.867111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:37.983772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.184236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.307905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.439553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.563223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.691879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.811559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:38.941212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.067873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.194535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.329175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.457831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.588481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.725116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.836818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:39.954530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.072201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.194887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.311548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.455166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.568860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.685576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.807258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:40.927900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.056558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.190200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.310876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.447513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.599105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.730753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.854423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:41.991085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.120738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.352094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.479779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.602423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.720108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.853778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:42.977448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.103084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.224786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.348428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.469105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.605741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.735393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.861086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:43.981766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.157293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.284960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.424552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.550242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.685853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.816503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:44.947182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.071820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.200504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.311180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.433852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.557521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.682190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.806857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:45.931548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.056190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.183847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.306519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.444152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.562835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.703459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.833119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:46.970743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:47.120344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:47.268947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:47.397602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:47.644941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:47.769634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:47.900285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.019938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.160591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.283234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.412888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.533564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.668204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.787884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:48.912551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.038249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.204770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.345393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.497986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.642599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.799182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:49.944792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:50.082423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:50.219059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-11-15T16:19:54.239300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-15T16:19:54.464698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-15T16:19:54.682118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-15T16:19:54.905520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-15T16:19:50.524242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-15T16:19:50.828343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

RINaMgAlSiKCaBaFeType
01.5210113.644.491.1071.780.068.750.00.001
11.5176113.893.601.3672.730.487.830.00.001
21.5161813.533.551.5472.990.397.780.00.001
31.5176613.213.691.2972.610.578.220.00.001
41.5174213.273.621.2473.080.558.070.00.001
51.5159612.793.611.6272.970.648.070.00.261
61.5174313.303.601.1473.090.588.170.00.001
71.5175613.153.611.0573.240.578.240.00.001
81.5191814.043.581.3772.080.568.300.00.001
91.5175513.003.601.3672.990.578.400.00.111

Last rows

RINaMgAlSiKCaBaFeType
2041.5161714.950.02.2773.300.008.710.670.07
2051.5173214.950.01.8072.990.008.611.550.07
2061.5164514.940.01.8773.110.008.671.380.07
2071.5183114.390.01.8272.861.416.472.880.07
2081.5164014.370.02.7472.850.009.450.540.07
2091.5162314.140.02.8872.610.089.181.060.07
2101.5168514.920.01.9973.060.008.401.590.07
2111.5206514.360.02.0273.420.008.441.640.07
2121.5165114.380.01.9473.610.008.481.570.07
2131.5171114.230.02.0873.360.008.621.670.07

Duplicate rows

Most frequent

RINaMgAlSiKCaBaFeTypecount
01.5221314.213.820.4771.770.119.570.00.012